English(EN) RA-MoWE: Workflow-Affinity Embeddings for Query Clustering and Agentic Workflow Generation

新的RA-MoWE框架通过查询聚类增强LLM代理工作流

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-07 04:00

研究人员开发了RA-MoWE，一个旨在提高大型语言模型（LLM）代理工作流效率和有效性的新框架。RA-MoWE利用面向工作流亲和力的嵌入来聚类查询，从而能够生成针对特定推理策略的可重用专家工作流。该方法旨在平衡任务集合工作流的优化与查询特定推理的需求，在基准测试中表现优于现有方法。 AI

影响该框架可能带来更高效、更专业的LLM代理工作流，提高复杂任务的性能。

排序理由该集群包含一篇详细介绍LLM代理工作流新框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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本文如何被排名

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arXiv cs.AI TIER_1 English(EN) · Qi Cheng, Shengyu Chen, Wei Cheng, Yiqun Xie, Haoyu Wang, Haifeng Chen, Xiaowei Jia · 2026-10-07 04:00

RA-MoWE：用于查询聚类和代理工作流生成的与工作流亲和的嵌入

arXiv:2610.07851v1 Announce Type: new Abstract: Agentic workflows enable large language models (LLMs) to solve complex tasks by coordinating reasoning, tool use, and verification. However, a workflow optimized for an entire task collection can overlook differences in the reasonin…